Overview

Dataset statistics

Number of variables20
Number of observations476
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.5 KiB
Average record size in memory160.3 B

Variable types

NUM19
BOOL1

Reproduction

Analysis started2020-08-25 01:14:24.225445
Analysis finished2020-08-25 01:15:16.089230
Duration51.86 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

f44 is highly correlated with f104 and 1 other fieldsHigh correlation
f104 is highly correlated with f44High correlation
f14 is highly correlated with f44High correlation
f118 has 6 (1.3%) zeros Zeros

Variables

f136
Real number (ℝ)

Distinct count158
Unique (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-45.76470588235294
Minimum-197.0
Maximum137.0
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2020-08-25T01:15:16.132864image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-197
5-th percentile-135.25
Q1-91
median-36
Q3-23
95-th percentile70
Maximum137
Range334
Interquartile range (IQR)68

Descriptive statistics

Standard deviation62.47006268
Coefficient of variation (CV)-1.365027076
Kurtosis0.4075321011
Mean-45.76470588
Median Absolute Deviation (MAD)37
Skewness0.2075367297
Sum-21784
Variance3902.508731
2020-08-25T01:15:16.240743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-29204.2%
 
-24173.6%
 
-28163.4%
 
-25122.5%
 
-27112.3%
 
-36102.1%
 
-100102.1%
 
-3091.9%
 
-9981.7%
 
-9771.5%
 
-2371.5%
 
-1961.3%
 
-4661.3%
 
-3961.3%
 
-13461.3%
 
-19561.3%
 
-1761.3%
 
-2661.3%
 
-8961.3%
 
-13351.1%
 
-7751.1%
 
-9651.1%
 
7051.1%
 
-9851.1%
 
-3451.1%
 
Other values (133)27156.9%
 
ValueCountFrequency (%) 
-19710.2%
 
-19640.8%
 
-19561.3%
 
-19320.4%
 
-16010.2%
 
-15920.4%
 
-15710.2%
 
-15410.2%
 
-14810.2%
 
-13820.4%
 
ValueCountFrequency (%) 
13710.2%
 
13620.4%
 
13010.2%
 
11510.2%
 
9820.4%
 
9510.2%
 
9120.4%
 
9020.4%
 
8710.2%
 
8220.4%
 

f29
Real number (ℝ)

Distinct count157
Unique (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.611344537815126
Minimum-140.0
Maximum181.0
Zeros2
Zeros (%)0.4%
Memory size3.8 KiB
2020-08-25T01:15:16.362447image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-140
5-th percentile-96.25
Q1-31.25
median7
Q325
95-th percentile111
Maximum181
Range321
Interquartile range (IQR)56.25

Descriptive statistics

Standard deviation58.07717368
Coefficient of variation (CV)22.24033361
Kurtosis0.4679116786
Mean2.611344538
Median Absolute Deviation (MAD)26
Skewness-0.03307464733
Sum1243
Variance3372.958103
2020-08-25T01:15:16.473421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
10377.8%
 
9316.5%
 
7153.2%
 
6142.9%
 
-140132.7%
 
-64102.1%
 
11102.1%
 
-3891.9%
 
481.7%
 
381.7%
 
-6371.5%
 
-471.5%
 
12071.5%
 
-271.5%
 
1461.3%
 
-5361.3%
 
861.3%
 
551.1%
 
1551.1%
 
11951.1%
 
-6551.1%
 
-1051.1%
 
7440.8%
 
-1640.8%
 
240.8%
 
Other values (132)23850.0%
 
ValueCountFrequency (%) 
-140132.7%
 
-13930.6%
 
-11710.2%
 
-11210.2%
 
-10410.2%
 
-10110.2%
 
-9830.6%
 
-9710.2%
 
-9610.2%
 
-9520.4%
 
ValueCountFrequency (%) 
18110.2%
 
17310.2%
 
14410.2%
 
13010.2%
 
12510.2%
 
12410.2%
 
12110.2%
 
12071.5%
 
11951.1%
 
11610.2%
 

f118
Real number (ℝ)

ZEROS

Distinct count185
Unique (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-36.53781512605042
Minimum-209.0
Maximum137.0
Zeros6
Zeros (%)1.3%
Memory size3.8 KiB
2020-08-25T01:15:16.589484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-209
5-th percentile-203
Q1-151.75
median7.5
Q353
95-th percentile85.5
Maximum137
Range346
Interquartile range (IQR)204.75

Descriptive statistics

Standard deviation106.2290506
Coefficient of variation (CV)-2.907372821
Kurtosis-1.283989879
Mean-36.53781513
Median Absolute Deviation (MAD)53
Skewness-0.4937780468
Sum-17392
Variance11284.6112
2020-08-25T01:15:16.694691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
53142.9%
 
59142.9%
 
61102.1%
 
60102.1%
 
2591.9%
 
-20191.9%
 
-19891.9%
 
6381.7%
 
2781.7%
 
2481.7%
 
-20281.7%
 
-20671.5%
 
-20371.5%
 
5871.5%
 
5571.5%
 
-20461.3%
 
2161.3%
 
-18961.3%
 
-161.3%
 
061.3%
 
3661.3%
 
3561.3%
 
-19751.1%
 
-19251.1%
 
5651.1%
 
Other values (160)28459.7%
 
ValueCountFrequency (%) 
-20910.2%
 
-20840.8%
 
-20720.4%
 
-20671.5%
 
-20530.6%
 
-20461.3%
 
-20371.5%
 
-20281.7%
 
-20191.9%
 
-20030.6%
 
ValueCountFrequency (%) 
13710.2%
 
13610.2%
 
13510.2%
 
13020.4%
 
12920.4%
 
12810.2%
 
12610.2%
 
12330.6%
 
12010.2%
 
11610.2%
 

f28
Real number (ℝ)

Distinct count142
Unique (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-98.58193277310924
Minimum-164.0
Maximum147.0
Zeros1
Zeros (%)0.2%
Memory size3.8 KiB
2020-08-25T01:15:16.817450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-164
5-th percentile-160
Q1-150.25
median-117.5
Q3-63
95-th percentile5.25
Maximum147
Range311
Interquartile range (IQR)87.25

Descriptive statistics

Standard deviation63.84632545
Coefficient of variation (CV)-0.647647329
Kurtosis3.799229462
Mean-98.58193277
Median Absolute Deviation (MAD)38.5
Skewness1.768539521
Sum-46925
Variance4076.353273
2020-08-25T01:15:16.927003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-157194.0%
 
-156183.8%
 
-161132.7%
 
-159112.3%
 
-118112.3%
 
-155102.1%
 
-119102.1%
 
-160102.1%
 
-15481.7%
 
-6481.7%
 
-11681.7%
 
-15881.7%
 
-15371.5%
 
-12771.5%
 
-12871.5%
 
-6971.5%
 
-14771.5%
 
-13061.3%
 
-11761.3%
 
-5461.3%
 
-6561.3%
 
-6061.3%
 
-12061.3%
 
-4761.3%
 
-4861.3%
 
Other values (117)25954.4%
 
ValueCountFrequency (%) 
-16420.4%
 
-16330.6%
 
-16240.8%
 
-161132.7%
 
-160102.1%
 
-159112.3%
 
-15881.7%
 
-157194.0%
 
-156183.8%
 
-155102.1%
 
ValueCountFrequency (%) 
14720.4%
 
14510.2%
 
14310.2%
 
13910.2%
 
13010.2%
 
12620.4%
 
12510.2%
 
12310.2%
 
12010.2%
 
11820.4%
 

f141
Real number (ℝ)

Distinct count179
Unique (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-56.01890756302521
Minimum-294.0
Maximum238.0
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2020-08-25T01:15:17.048467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-294
5-th percentile-170.25
Q1-91
median-55
Q3-39
95-th percentile79
Maximum238
Range532
Interquartile range (IQR)52

Descriptive statistics

Standard deviation75.9554555
Coefficient of variation (CV)-1.355889624
Kurtosis1.748585426
Mean-56.01890756
Median Absolute Deviation (MAD)25
Skewness-0.1066229879
Sum-26665
Variance5769.231221
2020-08-25T01:15:17.148043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-42163.4%
 
-68142.9%
 
-40132.7%
 
-39112.3%
 
-43112.3%
 
-44112.3%
 
-69102.1%
 
-4681.7%
 
-3881.7%
 
-4181.7%
 
-4771.5%
 
-5671.5%
 
-4571.5%
 
-4861.3%
 
-6661.3%
 
-8261.3%
 
-5361.3%
 
-4961.3%
 
-6151.1%
 
-12451.1%
 
-5551.1%
 
-13240.8%
 
-5740.8%
 
7040.8%
 
-7640.8%
 
Other values (154)28459.7%
 
ValueCountFrequency (%) 
-29410.2%
 
-29140.8%
 
-29040.8%
 
-28910.2%
 
-25410.2%
 
-25210.2%
 
-24810.2%
 
-24710.2%
 
-22310.2%
 
-22110.2%
 
ValueCountFrequency (%) 
23810.2%
 
16710.2%
 
14810.2%
 
12510.2%
 
12310.2%
 
9210.2%
 
9140.8%
 
9020.4%
 
8920.4%
 
8720.4%
 

f160
Real number (ℝ)

Distinct count126
Unique (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-25.23109243697479
Minimum-135.0
Maximum185.0
Zeros1
Zeros (%)0.2%
Memory size3.8 KiB
2020-08-25T01:15:17.254107image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-135
5-th percentile-131
Q1-91
median-15
Q322
95-th percentile53.25
Maximum185
Range320
Interquartile range (IQR)113

Descriptive statistics

Standard deviation65.58829452
Coefficient of variation (CV)-2.599502764
Kurtosis-0.4626656329
Mean-25.23109244
Median Absolute Deviation (MAD)39
Skewness-0.2458923085
Sum-12010
Variance4301.824379
2020-08-25T01:15:17.359191image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-15285.9%
 
-124204.2%
 
-16183.8%
 
-123173.6%
 
-133142.9%
 
-130132.7%
 
21132.7%
 
-30112.3%
 
5091.9%
 
-2091.9%
 
-1391.9%
 
2491.9%
 
-2181.7%
 
2281.7%
 
-12571.5%
 
-12071.5%
 
3261.3%
 
-13161.3%
 
-3161.3%
 
-3261.3%
 
-1961.3%
 
5261.3%
 
-11661.3%
 
1961.3%
 
4961.3%
 
Other values (101)22246.6%
 
ValueCountFrequency (%) 
-13520.4%
 
-13410.2%
 
-133142.9%
 
-13220.4%
 
-13161.3%
 
-130132.7%
 
-12830.6%
 
-12571.5%
 
-124204.2%
 
-123173.6%
 
ValueCountFrequency (%) 
18530.6%
 
11510.2%
 
10710.2%
 
10010.2%
 
9910.2%
 
9810.2%
 
9710.2%
 
9510.2%
 
9210.2%
 
9010.2%
 

f163
Real number (ℝ≥0)

Distinct count112
Unique (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.36344537815125
Minimum35.0
Maximum302.0
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2020-08-25T01:15:17.481764image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile42
Q154
median69
Q399
95-th percentile173
Maximum302
Range267
Interquartile range (IQR)45

Descriptive statistics

Standard deviation47.48757453
Coefficient of variation (CV)0.5562987098
Kurtosis5.230639412
Mean85.36344538
Median Absolute Deviation (MAD)21
Skewness2.031177448
Sum40633
Variance2255.069735
2020-08-25T01:15:17.587990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
46234.8%
 
173194.0%
 
67173.6%
 
56153.2%
 
55153.2%
 
78142.9%
 
42132.7%
 
69122.5%
 
64112.3%
 
68112.3%
 
65102.1%
 
79102.1%
 
110102.1%
 
41102.1%
 
45102.1%
 
43102.1%
 
5791.9%
 
5381.7%
 
8581.7%
 
11171.5%
 
4871.5%
 
9771.5%
 
4971.5%
 
9971.5%
 
17261.3%
 
Other values (87)20042.0%
 
ValueCountFrequency (%) 
3510.2%
 
3940.8%
 
4040.8%
 
41102.1%
 
42132.7%
 
43102.1%
 
4451.1%
 
45102.1%
 
46234.8%
 
4761.3%
 
ValueCountFrequency (%) 
30220.4%
 
30110.2%
 
30010.2%
 
29410.2%
 
29310.2%
 
29120.4%
 
20910.2%
 
20720.4%
 
20510.2%
 
17420.4%
 

f50
Real number (ℝ)

Distinct count160
Unique (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-75.81512605042016
Minimum-273.0
Maximum214.0
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2020-08-25T01:15:17.706367image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-273
5-th percentile-203
Q1-106
median-86
Q3-32
95-th percentile-3.75
Maximum214
Range487
Interquartile range (IQR)74

Descriptive statistics

Standard deviation66.35892406
Coefficient of variation (CV)-0.8752728844
Kurtosis3.092367433
Mean-75.81512605
Median Absolute Deviation (MAD)34
Skewness0.1454548863
Sum-36088
Variance4403.506802
2020-08-25T01:15:17.987291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-22153.2%
 
-24112.3%
 
-112112.3%
 
-105102.1%
 
-102102.1%
 
-104102.1%
 
-26102.1%
 
-9991.9%
 
-9691.9%
 
-10391.9%
 
-9781.7%
 
-11081.7%
 
-2381.7%
 
-2181.7%
 
-2571.5%
 
-8671.5%
 
-10671.5%
 
-4461.3%
 
-9261.3%
 
-2861.3%
 
-9161.3%
 
-8961.3%
 
-4961.3%
 
-9861.3%
 
-12061.3%
 
Other values (135)27156.9%
 
ValueCountFrequency (%) 
-27340.8%
 
-27130.6%
 
-25910.2%
 
-25710.2%
 
-25510.2%
 
-24710.2%
 
-24610.2%
 
-24410.2%
 
-23610.2%
 
-23410.2%
 
ValueCountFrequency (%) 
21410.2%
 
20810.2%
 
17610.2%
 
13010.2%
 
10910.2%
 
10810.2%
 
10720.4%
 
10310.2%
 
10010.2%
 
9410.2%
 

f104
Real number (ℝ)

HIGH CORRELATION

Distinct count161
Unique (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-98.32352941176471
Minimum-320.0
Maximum95.0
Zeros1
Zeros (%)0.2%
Memory size3.8 KiB
2020-08-25T01:15:18.091147image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-320
5-th percentile-306
Q1-112
median-68.5
Q3-57
95-th percentile0.75
Maximum95
Range415
Interquartile range (IQR)55

Descriptive statistics

Standard deviation85.91261382
Coefficient of variation (CV)-0.8737747143
Kurtosis1.438189414
Mean-98.32352941
Median Absolute Deviation (MAD)16.5
Skewness-1.227810596
Sum-46802
Variance7380.977214
2020-08-25T01:15:18.182601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-60255.3%
 
-61224.6%
 
-59194.0%
 
-69183.8%
 
-54142.9%
 
-57142.9%
 
-56112.3%
 
-58102.1%
 
-53102.1%
 
-6291.9%
 
-7081.7%
 
-6381.7%
 
-5281.7%
 
-5571.5%
 
-10671.5%
 
-8561.3%
 
-6861.3%
 
-7861.3%
 
-6551.1%
 
-11251.1%
 
-30951.1%
 
-4351.1%
 
-6651.1%
 
-6740.8%
 
-10740.8%
 
Other values (136)23549.4%
 
ValueCountFrequency (%) 
-32010.2%
 
-31910.2%
 
-31710.2%
 
-31610.2%
 
-31430.6%
 
-31320.4%
 
-31220.4%
 
-30951.1%
 
-30840.8%
 
-30710.2%
 
ValueCountFrequency (%) 
9520.4%
 
9410.2%
 
9310.2%
 
9020.4%
 
6710.2%
 
6610.2%
 
6510.2%
 
6210.2%
 
5810.2%
 
5310.2%
 

f59
Real number (ℝ)

Distinct count164
Unique (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.056722689075631
Minimum-173.0
Maximum181.0
Zeros1
Zeros (%)0.2%
Memory size3.8 KiB
2020-08-25T01:15:18.298295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-173
5-th percentile-116
Q1-20
median5
Q349
95-th percentile126.25
Maximum181
Range354
Interquartile range (IQR)69

Descriptive statistics

Standard deviation66.48164715
Coefficient of variation (CV)7.340585489
Kurtosis0.5997541027
Mean9.056722689
Median Absolute Deviation (MAD)32
Skewness-0.3256404756
Sum4311
Variance4419.809407
2020-08-25T01:15:18.408070image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-17204.2%
 
-18194.0%
 
-20163.4%
 
-19142.9%
 
6132.7%
 
-391.9%
 
-2491.9%
 
13091.9%
 
6481.7%
 
-2161.3%
 
4061.3%
 
-2261.3%
 
-1661.3%
 
6851.1%
 
-17251.1%
 
-2951.1%
 
1451.1%
 
-17051.1%
 
4751.1%
 
551.1%
 
-2351.1%
 
-551.1%
 
4851.1%
 
2040.8%
 
-8240.8%
 
Other values (139)27758.2%
 
ValueCountFrequency (%) 
-17310.2%
 
-17251.1%
 
-17130.6%
 
-17051.1%
 
-16920.4%
 
-12040.8%
 
-11920.4%
 
-11710.2%
 
-11620.4%
 
-11520.4%
 
ValueCountFrequency (%) 
18110.2%
 
16010.2%
 
15910.2%
 
15320.4%
 
15210.2%
 
14410.2%
 
13440.8%
 
13220.4%
 
13091.9%
 
12910.2%
 

f14
Real number (ℝ)

HIGH CORRELATION

Distinct count165
Unique (%)34.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-114.68067226890756
Minimum-348.0
Maximum72.0
Zeros2
Zeros (%)0.4%
Memory size3.8 KiB
2020-08-25T01:15:18.526765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-348
5-th percentile-299
Q1-125.25
median-97
Q3-82
95-th percentile8
Maximum72
Range420
Interquartile range (IQR)43.25

Descriptive statistics

Standard deviation83.22949202
Coefficient of variation (CV)-0.7257499487
Kurtosis1.47146867
Mean-114.6806723
Median Absolute Deviation (MAD)19
Skewness-0.8238104523
Sum-54588
Variance6927.148341
2020-08-25T01:15:18.621482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-107132.7%
 
-80132.7%
 
-109122.5%
 
-88122.5%
 
-95112.3%
 
-89102.1%
 
-94102.1%
 
-9291.9%
 
-11391.9%
 
-9091.9%
 
-8291.9%
 
-9681.7%
 
-11671.5%
 
-9171.5%
 
-12771.5%
 
-12471.5%
 
-8771.5%
 
-11171.5%
 
-11771.5%
 
-8371.5%
 
-8161.3%
 
-9361.3%
 
-13161.3%
 
-8561.3%
 
-11961.3%
 
Other values (140)26555.7%
 
ValueCountFrequency (%) 
-34820.4%
 
-34510.2%
 
-34420.4%
 
-33410.2%
 
-33110.2%
 
-32940.8%
 
-32810.2%
 
-32510.2%
 
-32410.2%
 
-32110.2%
 
ValueCountFrequency (%) 
7220.4%
 
7130.6%
 
7020.4%
 
6910.2%
 
6810.2%
 
6710.2%
 
6510.2%
 
6410.2%
 
6310.2%
 
6220.4%
 

f155
Real number (ℝ)

Distinct count175
Unique (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.222689075630253
Minimum-145.0
Maximum161.0
Zeros1
Zeros (%)0.2%
Memory size3.8 KiB
2020-08-25T01:15:18.728056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-145
5-th percentile-99.25
Q1-50.25
median3
Q3104.5
95-th percentile125
Maximum161
Range306
Interquartile range (IQR)154.75

Descriptive statistics

Standard deviation78.49996842
Coefficient of variation (CV)4.838899892
Kurtosis-1.298060287
Mean16.22268908
Median Absolute Deviation (MAD)64.5
Skewness0.1317336027
Sum7722
Variance6162.245042
2020-08-25T01:15:18.843287image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
124336.9%
 
125214.4%
 
117142.9%
 
-67102.1%
 
118102.1%
 
-4491.9%
 
-5681.7%
 
-5481.7%
 
-4571.5%
 
-2271.5%
 
-2371.5%
 
771.5%
 
-2161.3%
 
3361.3%
 
10761.3%
 
-1061.3%
 
-5361.3%
 
11151.1%
 
-6851.1%
 
12351.1%
 
-6951.1%
 
9651.1%
 
-4651.1%
 
3151.1%
 
9751.1%
 
Other values (150)26555.7%
 
ValueCountFrequency (%) 
-14510.2%
 
-13810.2%
 
-13310.2%
 
-13120.4%
 
-12920.4%
 
-12610.2%
 
-12410.2%
 
-11810.2%
 
-11710.2%
 
-10820.4%
 
ValueCountFrequency (%) 
16110.2%
 
15910.2%
 
15610.2%
 
15410.2%
 
15310.2%
 
14910.2%
 
12620.4%
 
125214.4%
 
124336.9%
 
12351.1%
 

f132
Real number (ℝ)

Distinct count161
Unique (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-50.21218487394958
Minimum-140.0
Maximum170.0
Zeros2
Zeros (%)0.4%
Memory size3.8 KiB
2020-08-25T01:15:18.960722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-140
5-th percentile-138
Q1-120
median-103
Q316
95-th percentile106
Maximum170
Range310
Interquartile range (IQR)136

Descriptive statistics

Standard deviation87.18007461
Coefficient of variation (CV)-1.736233443
Kurtosis-1.001895591
Mean-50.21218487
Median Absolute Deviation (MAD)34
Skewness0.7163260577
Sum-23901
Variance7600.365409
2020-08-25T01:15:19.068948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-138316.5%
 
-111153.2%
 
-119132.7%
 
-120122.5%
 
-121102.1%
 
-128102.1%
 
-106102.1%
 
-113102.1%
 
-10991.9%
 
-11291.9%
 
-13791.9%
 
10691.9%
 
-10481.7%
 
-11871.5%
 
-13071.5%
 
-10871.5%
 
10561.3%
 
661.3%
 
5461.3%
 
-451.1%
 
-11551.1%
 
6051.1%
 
5951.1%
 
5551.1%
 
-12951.1%
 
Other values (136)25252.9%
 
ValueCountFrequency (%) 
-14030.6%
 
-13951.1%
 
-138316.5%
 
-13791.9%
 
-13620.4%
 
-13530.6%
 
-13420.4%
 
-13320.4%
 
-13220.4%
 
-13140.8%
 
ValueCountFrequency (%) 
17010.2%
 
14920.4%
 
14810.2%
 
13210.2%
 
12210.2%
 
12130.6%
 
12010.2%
 
11910.2%
 
11710.2%
 
11510.2%
 

f44
Real number (ℝ)

HIGH CORRELATION

Distinct count151
Unique (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-101.6281512605042
Minimum-343.0
Maximum81.0
Zeros1
Zeros (%)0.2%
Memory size3.8 KiB
2020-08-25T01:15:19.188864image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-343
5-th percentile-319.25
Q1-99.25
median-75
Q3-64
95-th percentile18.75
Maximum81
Range424
Interquartile range (IQR)35.25

Descriptive statistics

Standard deviation88.89022423
Coefficient of variation (CV)-0.8746614312
Kurtosis1.637994503
Mean-101.6281513
Median Absolute Deviation (MAD)14
Skewness-1.250056795
Sum-48375
Variance7901.471964
2020-08-25T01:15:19.285208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-67224.6%
 
-64173.6%
 
-62163.4%
 
-63153.2%
 
-60132.7%
 
-68122.5%
 
-71122.5%
 
-79122.5%
 
-74112.3%
 
-76102.1%
 
-6591.9%
 
-5991.9%
 
-6191.9%
 
-7591.9%
 
-8691.9%
 
-6991.9%
 
-8281.7%
 
-7081.7%
 
-7771.5%
 
-5871.5%
 
-8171.5%
 
-8561.3%
 
-7361.3%
 
-9161.3%
 
-9061.3%
 
Other values (126)22146.4%
 
ValueCountFrequency (%) 
-34310.2%
 
-34030.6%
 
-33520.4%
 
-33410.2%
 
-33210.2%
 
-32710.2%
 
-32610.2%
 
-32410.2%
 
-32351.1%
 
-32251.1%
 
ValueCountFrequency (%) 
8110.2%
 
8010.2%
 
7910.2%
 
7710.2%
 
7520.4%
 
7310.2%
 
7020.4%
 
6920.4%
 
6610.2%
 
6520.4%
 

f67
Real number (ℝ)

Distinct count33
Unique (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-161.48739495798318
Minimum-166.0
Maximum262.0
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2020-08-25T01:15:19.390082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-166
5-th percentile-165
Q1-165
median-165
Q3-164
95-th percentile-155.75
Maximum262
Range428
Interquartile range (IQR)1

Descriptive statistics

Standard deviation24.12122561
Coefficient of variation (CV)-0.1493690924
Kurtosis215.7389737
Mean-161.487395
Median Absolute Deviation (MAD)0
Skewness13.57533534
Sum-76868
Variance581.833525
2020-08-25T01:15:19.485706image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-16533269.7%
 
-1647916.6%
 
-16691.9%
 
-16271.5%
 
-16171.5%
 
-16051.1%
 
-15751.1%
 
-16330.6%
 
-15830.6%
 
-15320.4%
 
-12720.4%
 
5110.2%
 
-14510.2%
 
-12910.2%
 
-10710.2%
 
-15610.2%
 
26210.2%
 
-14710.2%
 
-10410.2%
 
-14910.2%
 
-4610.2%
 
-14210.2%
 
-15210.2%
 
-14810.2%
 
-13410.2%
 
Other values (8)81.7%
 
ValueCountFrequency (%) 
-16691.9%
 
-16533269.7%
 
-1647916.6%
 
-16330.6%
 
-16271.5%
 
-16171.5%
 
-16051.1%
 
-15910.2%
 
-15830.6%
 
-15751.1%
 
ValueCountFrequency (%) 
26210.2%
 
5110.2%
 
-2710.2%
 
-4610.2%
 
-10410.2%
 
-10710.2%
 
-12310.2%
 
-12720.4%
 
-12910.2%
 
-13410.2%
 

f84
Real number (ℝ)

Distinct count201
Unique (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.760504201680675
Minimum-98.0
Maximum251.0
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2020-08-25T01:15:19.583451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-98
5-th percentile-90
Q1-37
median63.5
Q3137
95-th percentile195
Maximum251
Range349
Interquartile range (IQR)174

Descriptive statistics

Standard deviation97.50341098
Coefficient of variation (CV)1.883741522
Kurtosis-1.455333527
Mean51.7605042
Median Absolute Deviation (MAD)85
Skewness0.07135597098
Sum24638
Variance9506.915153
2020-08-25T01:15:19.696015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
194122.5%
 
-18102.1%
 
-1691.9%
 
-1791.9%
 
-1981.7%
 
-3871.5%
 
19371.5%
 
13371.5%
 
-4271.5%
 
13761.3%
 
-4761.3%
 
19561.3%
 
12961.3%
 
-3761.3%
 
-9651.1%
 
14051.1%
 
-2351.1%
 
-4651.1%
 
12751.1%
 
-5151.1%
 
-2940.8%
 
-9540.8%
 
-1440.8%
 
11740.8%
 
12840.8%
 
Other values (176)32067.2%
 
ValueCountFrequency (%) 
-9820.4%
 
-9720.4%
 
-9651.1%
 
-9540.8%
 
-9410.2%
 
-9320.4%
 
-9230.6%
 
-9140.8%
 
-9030.6%
 
-8920.4%
 
ValueCountFrequency (%) 
25110.2%
 
23910.2%
 
22910.2%
 
22710.2%
 
22610.2%
 
22010.2%
 
21610.2%
 
21410.2%
 
21320.4%
 
20520.4%
 

f23
Real number (ℝ)

Distinct count213
Unique (%)44.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-57.502100840336134
Minimum-255.0
Maximum187.0
Zeros1
Zeros (%)0.2%
Memory size3.8 KiB
2020-08-25T01:15:19.813462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-255
5-th percentile-204.25
Q1-143
median-36.5
Q313.25
95-th percentile65
Maximum187
Range442
Interquartile range (IQR)156.25

Descriptive statistics

Standard deviation93.54169347
Coefficient of variation (CV)-1.626752625
Kurtosis-0.6847662988
Mean-57.50210084
Median Absolute Deviation (MAD)53
Skewness-0.2676367188
Sum-27371
Variance8750.048417
2020-08-25T01:15:19.911871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
15163.4%
 
18132.7%
 
13122.5%
 
14122.5%
 
17102.1%
 
981.7%
 
-6381.7%
 
171.5%
 
371.5%
 
-5061.3%
 
561.3%
 
5961.3%
 
-20261.3%
 
-18361.3%
 
-6561.3%
 
-2751.1%
 
1651.1%
 
-2251.1%
 
-8451.1%
 
751.1%
 
-20151.1%
 
-8140.8%
 
440.8%
 
-2840.8%
 
240.8%
 
Other values (188)30163.2%
 
ValueCountFrequency (%) 
-25530.6%
 
-25410.2%
 
-25310.2%
 
-25210.2%
 
-25110.2%
 
-25010.2%
 
-24920.4%
 
-24110.2%
 
-23910.2%
 
-23710.2%
 
ValueCountFrequency (%) 
18710.2%
 
18510.2%
 
14810.2%
 
14710.2%
 
14310.2%
 
14230.6%
 
13520.4%
 
13410.2%
 
13310.2%
 
12410.2%
 

f108
Real number (ℝ)

Distinct count217
Unique (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-110.93697478991596
Minimum-293.0
Maximum52.0
Zeros1
Zeros (%)0.2%
Memory size3.8 KiB
2020-08-25T01:15:20.018082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-293
5-th percentile-251.5
Q1-158
median-92
Q3-56
95-th percentile-7.75
Maximum52
Range345
Interquartile range (IQR)102

Descriptive statistics

Standard deviation75.28601057
Coefficient of variation (CV)-0.6786376744
Kurtosis-0.2862057327
Mean-110.9369748
Median Absolute Deviation (MAD)46
Skewness-0.5938161983
Sum-52806
Variance5667.983388
2020-08-25T01:15:20.131917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-82112.3%
 
-96102.1%
 
-9081.7%
 
-8071.5%
 
-9771.5%
 
-8771.5%
 
-8971.5%
 
-7061.3%
 
-5361.3%
 
-5061.3%
 
-5161.3%
 
-8161.3%
 
-9861.3%
 
-4751.1%
 
-7351.1%
 
-8651.1%
 
-4051.1%
 
-15351.1%
 
-8351.1%
 
-2451.1%
 
-9251.1%
 
-12451.1%
 
-12151.1%
 
-2051.1%
 
-12251.1%
 
Other values (192)32367.9%
 
ValueCountFrequency (%) 
-29330.6%
 
-29120.4%
 
-29020.4%
 
-28910.2%
 
-28830.6%
 
-28720.4%
 
-28510.2%
 
-28310.2%
 
-27810.2%
 
-27610.2%
 
ValueCountFrequency (%) 
5210.2%
 
5010.2%
 
3410.2%
 
3110.2%
 
1710.2%
 
1610.2%
 
1510.2%
 
1320.4%
 
1210.2%
 
1010.2%
 

f74
Real number (ℝ)

Distinct count202
Unique (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-92.65546218487395
Minimum-338.0
Maximum112.0
Zeros0
Zeros (%)0.0%
Memory size3.8 KiB
2020-08-25T01:15:20.245125image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-338
5-th percentile-258.5
Q1-116.25
median-83
Q3-60
95-th percentile52.5
Maximum112
Range450
Interquartile range (IQR)56.25

Descriptive statistics

Standard deviation81.87748866
Coefficient of variation (CV)-0.8836768683
Kurtosis1.293102988
Mean-92.65546218
Median Absolute Deviation (MAD)29
Skewness-0.6188958212
Sum-44104
Variance6703.923149
2020-08-25T01:15:20.345055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-67102.1%
 
-75102.1%
 
-8491.9%
 
-6581.7%
 
-10781.7%
 
-4781.7%
 
-6281.7%
 
-8781.7%
 
-6171.5%
 
-5171.5%
 
-10171.5%
 
-6371.5%
 
-8371.5%
 
-7371.5%
 
-11261.3%
 
-6861.3%
 
-9961.3%
 
-6961.3%
 
-12061.3%
 
-5351.1%
 
-11751.1%
 
-7751.1%
 
-7151.1%
 
-10351.1%
 
-9151.1%
 
Other values (177)30564.1%
 
ValueCountFrequency (%) 
-33820.4%
 
-33510.2%
 
-33410.2%
 
-32910.2%
 
-32510.2%
 
-32410.2%
 
-32210.2%
 
-31810.2%
 
-31710.2%
 
-31610.2%
 
ValueCountFrequency (%) 
11210.2%
 
8610.2%
 
8420.4%
 
8310.2%
 
8210.2%
 
8010.2%
 
7810.2%
 
7710.2%
 
7410.2%
 
7320.4%
 

target
Boolean

Distinct count2
Unique (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
0
269
1
207
ValueCountFrequency (%) 
026956.5%
 
120743.5%
 

Interactions

2020-08-25T01:14:25.430668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:25.577083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:25.717525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:25.861798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:26.001971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:26.140641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:26.285590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:26.423767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:26.555204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:26.689075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:26.828377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:27.135467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:27.277501image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:27.417953image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:27.555145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:27.688955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:27.835695image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:27.976293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:28.129235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:28.267336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:28.405728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:28.540782image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:28.684367image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:28.823798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:28.955954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:29.094870image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:29.230323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:29.357104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:29.488750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:29.629510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:29.753605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:29.890431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:30.022334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:30.151396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T01:14:59.057274image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:59.203718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:59.331057image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:59.452588image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:59.592365image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:59.723933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:59.859328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:14:59.987134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:00.124079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:00.253717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:00.380839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:00.507985image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:00.633639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:00.763288image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:00.889228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:01.005741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:01.121454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:01.257910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:01.369953image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:01.672522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:01.797086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:01.914538image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:02.027934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:02.154147image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:02.268954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:02.395776image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:02.524530image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:02.644227image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:02.766560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:02.888832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:03.008064image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:03.120849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:03.246267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:03.362612image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:03.471389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:03.581767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:03.700663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:03.812690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:03.931322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:04.046605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:04.161885image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:04.271207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:04.392376image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:04.515815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:04.647063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:04.757959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:04.894779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:05.031389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:05.175092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:05.316042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:05.456132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:05.785624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:05.921335image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:06.054858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:06.188437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:06.329741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:06.460975image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:06.602897image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:06.744615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:06.882411image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:07.010140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:07.152208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:07.284752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:07.429091image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:07.560530image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:07.690491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:07.817551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:07.943379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:08.068167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:08.193449image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:08.327217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:08.450280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:08.563533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:08.682067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:08.807305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:08.923174image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:09.047411image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:09.171436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:09.285892image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:09.402800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:09.531670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:09.647650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:09.957884image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:10.072616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:10.216295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:10.359429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:10.509155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:10.663202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:10.815387image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:10.962743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:11.103737image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:11.238516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:11.372994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:11.514601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:11.643875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:11.785105image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:11.926164image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:12.059252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:12.188188image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:12.335142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:12.466294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:12.618652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:12.751831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:12.886929image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:13.016176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:13.146533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:13.276194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:13.399204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:13.528668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:13.652516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:13.771537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:13.901021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:14.028516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:14.325812image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:14.451267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:14.577454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:14.694605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:14.809093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:14.941711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:15.061010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:15.193782image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:15:20.493325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T01:15:20.795933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T01:15:21.278959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T01:15:21.591049image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T01:15:15.465247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:15:15.928608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

f136f29f118f28f141f160f163f50f104f59f14f155f132f44f67f84f23f108f74target
0-19.025.025.0-127.0-100.0-120.048.0-105.0-289.035.0-286.0-54.0-138.0-282.0-165.0-38.0-50.0-17.0-246.01
1-89.0-63.036.0-64.0-29.0-120.048.0-4.0-309.03.0-281.0-54.0-138.0-323.0-165.0115.01.0-122.0-194.01
2-89.025.037.0-64.0-29.0-120.048.0-105.0-280.035.0-274.0-54.0-138.0-293.0-165.0-37.01.0-122.0-194.01
3-19.0-63.024.0-128.0-100.0-120.048.0-4.0-282.02.0-280.0-54.0-138.0-286.0-165.0116.0-50.0-16.0-246.01
4-20.025.024.0-128.0-99.051.043.0-105.0-289.034.0-286.097.0-138.0-283.0-165.0-38.0-50.0-16.0-246.01
5-90.0-64.036.0-64.0-28.051.042.0-4.0-308.03.0-280.097.0-138.0-322.0-165.0115.01.0-121.0-193.01
6-90.026.036.0-65.0-28.051.044.0-106.0-280.036.0-274.0100.0-138.0-292.0-165.0-38.00.0-121.0-193.01
7-20.0-64.023.0-128.0-99.051.042.0-4.0-281.03.0-280.097.0-138.0-285.0-165.0115.0-50.0-16.0-245.01
8-89.023.034.0-70.0-29.038.066.0-117.0-308.018.0-286.0119.0-128.0-321.0-165.0133.02.0-122.0-198.01
9-19.030.0-8.0-147.0-75.0-12.045.0-110.0-296.040.0-314.0-15.0-128.0-318.0-165.0-42.0-22.0-38.0-288.01

Last rows

f136f29f118f28f141f160f163f50f104f59f14f155f132f44f67f84f23f108f74target
466-195.062.0-204.0-157.0-290.0-66.0107.0-255.0-305.083.0-324.0122.043.0-322.0-165.0137.0-255.0-288.0-317.00
467-195.064.0-205.0-158.0-290.0-134.0164.0-257.0-304.084.0-325.0-12.0104.0-322.0-165.0136.0-255.0-287.0-318.00
468-80.09.0-70.0-156.0-44.027.076.0-91.0-53.0-19.0-126.0124.0-114.0-75.0-165.0-15.03.0-24.0-117.00
469-28.0-71.0-197.0-161.0-81.0-16.055.0-29.0-101.0-82.0-86.0-67.0-106.0-80.0-165.0251.0-174.0-191.0-93.00
470-23.0-101.0-188.0-155.0-86.0-20.099.0-23.0-109.0-88.0-83.0118.0-3.0-80.0-165.0128.0-177.0-212.0-85.00
471-89.010.0-91.0-157.0-41.033.047.0-87.0-54.0-18.0-134.031.0-108.0-79.0-155.0-21.015.0-16.0-104.00
472-136.09.0123.0102.0-64.020.079.0-98.0-61.0-18.0-109.0124.0-110.0-64.0-165.0-17.0-6.0-72.0-70.00
473-29.072.0-198.0-160.0-130.0-15.055.0-35.0-122.0159.0-82.0-66.0-104.0-100.0-46.0-61.0-201.0-164.0-67.00
47456.09.084.0147.0-70.013.079.0-79.0-58.0-20.0-112.0125.0-113.0-89.0-165.0-11.0-2.0-136.0-47.00
475-24.065.0-190.0-155.0-127.0-20.098.0-45.0-124.0153.0-83.0118.0-5.0-106.0-104.0-55.0-196.0-189.0-62.00